Explant Culture Analysis

Overall Objective

Load Libraries

library(tidyverse)
library(cowplot)
library(broom)

Import data

Convert data from ‘wide’ to ‘long’ format

Factor the data into categorical variables

# Refactoring Columns for samples
data2$Sample_ID <- as.factor(data2$Sample_ID)
data2$Dilution_factor <- as.numeric(data2$Dilution_factor)
data2$Injection<- as.factor(data2$Injection)
data2$Tech_rep <- as.numeric(data2$Tech_rep)
data2

# Refactoring COlumns for key
key$Sample_ID <- as.factor(key$Sample_ID)
key$Animal <- as.factor(key$Animal)
key$Condition <- as.factor(key$Condition)
key

# Refactoring columns for standards
standards2$Sample_ID <- as.factor(standards2$Sample_ID)
standards2$When <- as.factor(standards2$When)
standards2$Dilution_factor <- as.numeric(standards2$Dilution_factor)
standards2$Injection <- as.factor(standards2$Injection)
standards2$Nano_day <- as.numeric(standards2$Nano_day)
standards2

Back calculate standards

standards2 <- standards2 %>% 
  mutate(True_Count=Dilution_factor*Count)
# Set the correct order of 'categorical factors'
standards2$Nano_day <-  factor(standards2$Nano_day, levels=c('1'))
standards2$When <- factor(standards2$When, levels=c('before','after'))
standards2

Summarize three technical standard replicates

standards3 <- standards2 %>% 
  group_by(particle_size,Sample_ID,When,Dilution_factor,Nano_day,Injection) %>% 
  summarise( tech_N = length(True_Count),
             tech_mean = mean(True_Count),
             tech_sd = sd(True_Count),
             tech_se = tech_sd/sqrt(tech_N))
standards3

Summarize standards by injection

standards4 <- standards3 %>% 
  group_by(Nano_day,When,particle_size) %>% 
  summarise( inj_N = length(tech_mean),
             inj_mean = mean(tech_mean),
             inj_sd = sd(tech_mean),
             inj_se = inj_sd/sqrt(inj_N))
standards4

Plot before and after plots, facet by experimental day

std_plot <- standards4 %>% 
  ggplot(aes(x=particle_size,y=inj_mean,color=When))+
  geom_ribbon(aes(ymin=inj_mean-inj_se, ymax=inj_mean+inj_se),alpha=0.2,fill = alpha('grey12', 0.2)) + #error bars
  geom_line(size=2) + xlim(0,500)+ #line size, x-axis scale
  scale_y_continuous(expand=c(0,0))+ #set bottom of graph
  xlab("Particle Size") + # X axis label
  ylab("\nMean Particle Concentration/ml\n") + # Y axis label
  ggtitle("Nanosight Histogram of\nVirgin Mouse Plasma")+ #title
  labs(color="Condition")+ #Label table title
  facet_grid(. ~ When)
std_plot

Standards particle concentrations from each experimental day

standards_df <- standards4 %>% 
  group_by(Nano_day,When) %>% 
  summarise(total=sum(inj_mean))
standards_df

Bar graph of standards particle concentrations

standards_df %>% 
  ggplot(aes(x=Nano_day,y=total,fill=When))+
  geom_col(position="dodge")+
  scale_y_continuous(expand=c(0,0))+ #set bottom of graph
  xlab("Experimental Day") + # X axis label
  ylab("\nMean Particle Concentration/ml\n") + # Y axis label
  ggtitle("Nanosight Histogram of\nVirgin Mouse Plasma")+ #title
  labs(color="When") #Label table title

Intraassay variability

Intra.assay_cv <- standards_df %>% 
  group_by(Nano_day) %>% 
  summarise(Day_N = length(total),
             Day_mean = mean(total),
             Day_sd = sd(total),
             Day_se = Day_sd/sqrt(Day_N),
            Day_cv = Day_sd/Day_mean )
Intra.assay_cv

Sample analysis

Back calculate the original concentration of the sample

data2 <- data2 %>% 
  mutate(True_Count = Dilution_factor*Count)
data2

Average three technical readings

data3 <- data2 %>% 
  group_by(particle_size,Sample_ID,Dilution_factor,Injection) %>% 
  summarise( tech_N = length(True_Count),
             tech_mean = mean(True_Count),
             tech_sd = sd(True_Count),
             tech_se = tech_sd/sqrt(tech_N))
data3

Summarize samples by injection (average both injections)

data4 <- data3 %>% 
  group_by(particle_size,Sample_ID,Dilution_factor) %>% 
  summarise( inj_N = length(tech_mean),
             inj_mean = mean(tech_mean),
             inj_sd = sd(tech_mean),
             inj_se = inj_sd/sqrt(inj_N))
data4

# Average technical replicates and merge with key
merge <- left_join(key,data3, by= "Sample_ID")
merge

# Average injection replicates and merge with key
merge1 <- left_join(key,data4, by= "Sample_ID")
merge1

Quick visualizations

Graphing all samples

sample_plot <- merge %>%
  ggplot(aes(x=particle_size, y=tech_mean,color=Injection ))+ #plot
  geom_ribbon(aes(ymin=tech_mean-tech_se,
                  ymax=tech_mean+tech_se),
                  alpha=0.2,fill = alpha('grey12', 0.2)) + #error bars
  geom_line(size=2.0) + xlim(0,500)+ #line size, x-axis scale
  scale_y_continuous(expand=c(0,0))+ #set bottom of graph
  xlab("Particle Size") + # X axis label
  ylab("\nMean Particle Concentration/ml\n") + # Y axis label
  ggtitle("Nanosight Histogram of\nVirgin Mouse Plasma")+ #title
  labs(color="Injection")+ #Label table title
  facet_grid(Animal ~ Condition)+
  geom_vline(xintercept = 140)+
  annotate("text", x= 235, y = 1E9, label= "140nm")
sample_plot

Particle concentration values for each of the samples

merge2 <- merge1 %>% 
  group_by(Animal,Condition) %>% 
  summarise(particle_conc=sum(inj_mean))
merge2

Summary statistics of particle concentration (averaging n=6 for each time point)

merge3 <- merge2 %>% 
  group_by(Condition) %>% 
  summarise(Condition_N=length(particle_conc),
            Condition_mean = mean(particle_conc),
            Condition_sd = sd(particle_conc),
            Condition_se = Condition_sd/sqrt(Condition_N))
merge3

Boxplot

plot1 <- merge2 %>% 
  #filter(!Animal== '1371'|!Condition == 'lowOxygen') %>% 
  group_by(Condition) %>% 
  ggplot(aes(x= Condition, y = particle_conc, color=Condition)) +
  geom_boxplot(colour="black",fill=NA) + 
  geom_point(aes(text = paste("Animal:", Animal)),
             position='jitter',size=3)+
  xlab("\nTreatment\n") + # X axis label
  ylab("\nExosomes/ml\n") + # Y axis label
  ggtitle("GD 17.5 Placental Exosome \nExplant Culture (Ultracentrigution)\n")+ #title
  labs(color="Condition") # Label table title
  
plot1

Interactive Plot

ggplotly(plot1)

Statistics

fit <- t.test(particle_conc ~ Condition,data=merge2)
tidy(fit)